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Integer point sets minimizing average pairwise \(L_{1}\) distance: What is the optimal shape of a town? (English) Zbl 1208.65085

The authors study a special case of the task consisting in finding \(n\) sites from a given set of candidate locations, which minimizes the average distance between the sites. In the special case studied in the paper, a group of \(n\) buildings each occupying; a distinct site on a 2-dimensional integer grid is considered. Such group of buildings is called \(n\)-town and different positions of the building form different shapes of the \(n\)-town. The authors propose a procedure, which makes possible to find the optimal shape of the \(n\)-town, i.e. the shape, which minimizes the sum of all pairwise Manhattan distances between the buildings. Computational experience with the proposed method for values of \(n\) up to \(n= 80\) is presented in the concluding part of the paper.

MSC:

65K05 Numerical mathematical programming methods

Online Encyclopedia of Integer Sequences:

Optimal towns for n>=1.

References:

[1] Arora, S.; Karger, D. R.; Karpinski, M., Polynomial time approximation schemes for dense instances of NP-hard problems, J. Comput. Syst. Sci., 58, 1, 193-210 (1999) · Zbl 0937.68160
[2] Asahiro, Y.; Iwama, K.; Tamaki, H.; Tokuyama, T., Greedily finding a dense subgraph, J. Algorithms, 34, 2, 203-221 (2000) · Zbl 0958.68132
[3] Y. Bartal, M. Charikar, D. Raz, Approximating min-sum \(k\); Y. Bartal, M. Charikar, D. Raz, Approximating min-sum \(k\) · Zbl 1323.68565
[4] Bender, C. M.; Bender, M. A.; Demaine, E. D.; Fekete, S. P., What is the optimal shape of a city?, J. Phys. A: Math. Gen., 37, 147-159 (2004) · Zbl 1046.90538
[5] Bender, M. A.; Bunde, D. P.; Demaine, E. D.; Fekete, S. P.; Leung, V. J.; Meijer, H.; Phillips, C. A., Communication-aware processor allocation for supercomputers: Finding point sets of small average distance, Algorithmica, 50, 2, 279-298 (2008) · Zbl 1141.68017
[6] Fekete, S. P.; Meijer, H., Maximum dispersion and geometric maximum weight cliques, Algorithmica, 38, 501-511 (2003) · Zbl 1095.68082
[7] Fekete, S. P.; Mitchell, J. S.B.; Beurer, K., On the continuous Fermat-Weber problems, Oper. Res., 53, 61-76 (2005) · Zbl 1165.90553
[8] S.P. Fekete, J.S.B. Mitchell, K. Weinbrecht, On the continuous Weber and \(k\); S.P. Fekete, J.S.B. Mitchell, K. Weinbrecht, On the continuous Weber and \(k\) · Zbl 1377.90054
[9] Guttmann-Beck, N.; Hassin, R., Approximation algorithms for minimum sum \(p\)-clustering, Disc. Appl. Math., 89, 125-142 (1998) · Zbl 0921.68044
[10] Hassin, R.; Levin, A.; Sviridenko, M., Approximating the minimum quadratic assignment problems, ACM Trans. Algorithms, 6, 1, 18:1-18:10 (2009) · Zbl 1300.90024
[11] Hassin, R.; Rubinstein, S.; Tamir, A., Approximation algorithms for maximum dispersion, Oper. Res. Lett., 21, 3, 133-137 (1997) · Zbl 0888.90144
[12] P. Indyk, A sublinear time approximation scheme for clustering in metric spaces, in: Proc. 40th Annual IEEE Symposium on Foundations of Computer Science (FOCS), 1999, pp. 154-159.; P. Indyk, A sublinear time approximation scheme for clustering in metric spaces, in: Proc. 40th Annual IEEE Symposium on Foundations of Computer Science (FOCS), 1999, pp. 154-159.
[13] Karp, R. M.; McKellar, A. C.; Wong, C. K., Near-optimal solutions to a 2-dimensional placement problem, SIAM J. Comput., 4, 3, 271-286 (1975) · Zbl 0355.68044
[14] Kortsarz, G.; Peleg, D., On choosing a dense subgraph, (Proc. 34th Annual Symposium on Foundations of Computer Science (1993), IEEE Computer Society Press: IEEE Computer Society Press Palo Alto, CA), 692-703
[15] Krumke, S.; Marathe, M.; Noltemeier, H.; Radhakrishnan, V.; Ravi, S.; Rosenkrantz, D., Compact location problems, Theoret. Comput. Sci., 181, 2, 379-404 (1997) · Zbl 0902.90114
[16] V. Leung, E. Arkin, M. Bender, D. Bunde, J. Johnston, A. Lal, J. Mitchell, C. Phillips, S. Seiden, Processor allocation on Cplant: Achieving general processor locality using one-dimensional allocation strategies. in: Proc. 4th IEEE International Conference on Cluster Computing, 2002, pp. 296-304.; V. Leung, E. Arkin, M. Bender, D. Bunde, J. Johnston, A. Lal, J. Mitchell, C. Phillips, S. Seiden, Processor allocation on Cplant: Achieving general processor locality using one-dimensional allocation strategies. in: Proc. 4th IEEE International Conference on Cluster Computing, 2002, pp. 296-304.
[17] Loiola, E. M.; de Abreu, N. M.M.; Boaventura-Netto, P. O.; Hahn, P.; Querido, T. M., A survey for the quadratic assignment problem, Eur. J. Oper. Res., 176, 2, 657-690 (2007) · Zbl 1103.90058
[18] J. Mache, V. Lo, Dispersal metrics for non-contiguous processor allocation, Technical Report CIS-TR-96-13, University of Oregon, 1996.; J. Mache, V. Lo, Dispersal metrics for non-contiguous processor allocation, Technical Report CIS-TR-96-13, University of Oregon, 1996.
[19] J. Mache, V. Lo, The effects of dispersal on message-passing contention in processor allocation strategies, in: Proc. Third Joint Conference on Information Sciences, Sessions on Parallel and Distributed Processing, vol. 3, 1997, pp. 223-226.; J. Mache, V. Lo, The effects of dispersal on message-passing contention in processor allocation strategies, in: Proc. Third Joint Conference on Information Sciences, Sessions on Parallel and Distributed Processing, vol. 3, 1997, pp. 223-226.
[20] Ravi, S. S.; Rosenkrantz, D. J.; Tayi, G. K., Heuristic and special case algorithms for dispersion problems, Oper. Res., 42, 2, 299-310 (1994) · Zbl 0805.90074
[21] Sahni, S.; Gonzalez, T., \(P\)-complete approximation problems, J. ACM, 23, 3, 555-565 (1976) · Zbl 0348.90152
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